Instructions to use lemon07r/Qwen3-R1-SLERP-Q3T-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use lemon07r/Qwen3-R1-SLERP-Q3T-8B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="lemon07r/Qwen3-R1-SLERP-Q3T-8B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("lemon07r/Qwen3-R1-SLERP-Q3T-8B")
model = AutoModelForCausalLM.from_pretrained("lemon07r/Qwen3-R1-SLERP-Q3T-8B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use lemon07r/Qwen3-R1-SLERP-Q3T-8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "lemon07r/Qwen3-R1-SLERP-Q3T-8B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lemon07r/Qwen3-R1-SLERP-Q3T-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/lemon07r/Qwen3-R1-SLERP-Q3T-8B

SGLang

How to use lemon07r/Qwen3-R1-SLERP-Q3T-8B with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "lemon07r/Qwen3-R1-SLERP-Q3T-8B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lemon07r/Qwen3-R1-SLERP-Q3T-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "lemon07r/Qwen3-R1-SLERP-Q3T-8B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lemon07r/Qwen3-R1-SLERP-Q3T-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use lemon07r/Qwen3-R1-SLERP-Q3T-8B with Docker Model Runner:
```
docker model run hf.co/lemon07r/Qwen3-R1-SLERP-Q3T-8B
```

Trained context size and rope scaling inconsistency

by lukasstraub2 - opened Jul 30, 2025

Discussion

lukasstraub2

Jul 30, 2025

Hello,
Looking at gguf quants, I see that for both base models DeepSeek-R1-0528-Qwen3-8B and Qwen3, the trained context length qwen3.context_length is 131072 and qwen3.rope.scaling.original_context_length is 32768.

However, for this model qwen3.context_length is 40960 and rope scaling parameters look off too. Is this intended? Would rope scaling work with this model?

Best regards

lemon07r

Owner Jul 30, 2025

Hello,
Looking at gguf quants, I see that for both base models DeepSeek-R1-0528-Qwen3-8B and Qwen3, the trained context length qwen3.context_length is 131072 and qwen3.rope.scaling.original_context_length is 32768.

However, for this model qwen3.context_length is 40960 and rope scaling parameters look off too. Is this intended? Would rope scaling work with this model?

Best regards

This doesnt seem to be intended behaviour, should be the same as the parent models. Maybe an issue with mergekit.

lukasstraub2

Jul 30, 2025

I found out more. So first the official Qwen3 GGUFs do in fact have a qwen3.context_length of 40960 and it is explained in the Model Card:

The default max_position_embeddings in config.json is set to 40,960. This allocation includes reserving 32,768 tokens for outputs and 8,192 tokens for typical prompts, which is sufficient for most scenarios involving short text processing. If the average context length does not exceed 32,768 tokens, we do not recommend enabling YaRN in this scenario, as it may potentially degrade model performance.

Moreover, the official GGUFs also don't have the rope scaling parameters, you are supposed to supply them manually.

So I think this is actually as intended.

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